Body Composition May Be a Novel Presurgical Risk Factor for Acute Kidney Injury among Clear Cell Renal Cell Cancer Patients Undergoing Radical Nephrectomy
Bibliographic record
Abstract
Patients with renal cell carcinoma (RCC) undergoing nephrectomy are at risk for acute kidney injury (AKI). Prior studies have focused predominantly on nonmodifiable surgical AKI risk factors. We conducted the first investigation of body composition features and AKI to identify factors that could improve presurgical risk stratification and be targeted in future interventions. We analyzed data from 1199 patients with stages I–III, clear cell RCC undergoing radical (RN) or partial nephrectomy (PN) from 2000 to 2020. AKI was defined as a serum creatinine (sCr) increase by 0.3 mg/dL within 48 h or a 1.5-fold increase in sCr within 7 days. Preoperative computed tomography (CT) scans were segmented to determine quantities and radiodensities of adipose tissue and skeletal muscle using Automatica software. Multivariable generalized linear models estimated 7-day risk differences (RD) and 95% confidence intervals (CI) within surgical subgroups. AKI was more frequent among patients undergoing RN (66%) than PN (26%). For RN, only higher visceral adipose tissue (VAT) quantity was significantly associated with greater AKI risk (RD per 40-unit increase 5.2 [95% CI: 1.3, 9.2]). We initially detected a similar association in PN, but after multivariable adjustment for all body composition features, associations were attenuated and became nonsignificant. Associations between presurgical body composition and AKI risk vary by surgery type. Higher VAT quantity increased AKI risk only among RN patients. If confirmed, CT-derived VAT quantity may be a novel pre-surgical imaging characteristic that could be used to inform treatment selection or modified to decrease postoperative AKI risk in RN patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".